Being used in key features, such as sensing and intelligent path planning, Artificial Intelligence (AI) has become an inevitable part of automated vehicles (AVs). However, their usage in the automotive industry always comes with a “label” that questions their impact on the overall AV safety. This paper focuses on the safe deployment of AI-based AVs. Among the various ways for ensuring the safety of AI-based AVs is to monitor the safe execution of the system responsible for automated driving (i.e., Automated Driving System (ADS)) at runtime (i.e., runtime monitoring). Most of the research done in the past years focused on verifying whether the path or trajectory generated by the ADS does not immediately collide with objects on the road. However, as we will show in this paper, there are other unsafe situations that do not immediately result in a collision but the monitor should check for them. To build our case, we have looked into the National Highway Traffic Safety Administration (NHTSA) database of 5.9 million police-reported light-vehicle accidents and categorized these accidents into five main categories of unsafe vehicle operations. Furthermore, we have performed a high-level evaluation of the runtime monitoring approach proposed in [1], by estimating what percentage of the total population of 5.9 million of unsafe operations the approach would be able to detect. Lastly, we have performed the same evaluation on other existing runtime monitoring approaches to make a basic comparison of their diagnostic capabilities.

The paper shows that the information of the first just noticeable difference (JND) point position can significantly improve the performance of the objective peak signal-to-noise ratio (PSNR) measure in assessing the quality of JPEG compressed images. The degree of improvement depends on the choice of the first JND point position prediction model. Also, the paper shows that simple features derived from the gradient magnitude (spatial information and spatial frequency) of the original uncompressed image can be used for reliable position prediction. The analysis was conducted on two publicly available JND subject-rated image datasets MCL-JCI and JND-Pano. Among others, the linear correlation coefficient is used as an objective measurement parameter in prediction and in image quality assessment analysis. The prediction based on spatial frequency provided the best results, with over 95% of agreement with ground truth JND points position. This simple picture-wise prediction model has significantly improved the performance of conventional PSNR measure, with over 90% of agreement with subjective scores in image quality assessment. The PSNR performance is most enhanced by using a deep learning approach, where the correlation with subjective test results is close to 92%.

The expression pattern of Connexins (Cx) 37, 40, 43, 45 and Pannexin 1 (Pnx1) was analyzed immunohistochemically, as well as semi-quantitatively and quantitatively in histological sections of developing 8th- to 12th-week human eyes and postnatal healthy eye, in retinoblastoma and different uveal melanomas. Expressions of both Cx37 and Cx43 increased during development but diminished in the postnatal period, being higher in the retina than in the choroid. Cx37 was highly expressed in the choroid of retinoblastoma, and Cx43 in epitheloid melanoma, while they were both increasingly expressed in mixoid melanoma. In contrast, mild retinal Cx40 expression during development increased to strong in postnatal period, while it was significantly higher in the choroid of mixoid melanoma. Cx45 showed significantly higher expression in the developing retina compared to other samples, while it became low postnatally and in all types of melanoma. Pnx1 was increasingly expressed in developing choroid but became lower in the postnatal eye. It was strongly expressed in epithelial and spindle melanoma, and particularly in retinoblastoma. Our results indicate importance of Cx37 and Cx40 expression in normal and pathological vascularization, and Cx43 expression in inflammatory response. Whereas Cx45 is involved in early stages of eye development, Pnx1might influence cell metabolism. Additionally, Cx43 might be a potential biomarker of tumor prognosis.

Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale datasets. However, most of these datasets are collected from 2.4G WiFi devices and through similar channel environments. Meanwhile, they only provided receiving data collected by the specific equipment. This paper utilizes software radio peripheral as a dataset generating platform. Therefore, the user can customize the parameters of the dataset, such as frequency band, modulation mode, antenna gain, and so on. In addition, the proposed dataset is generated through various and complex channel environments, which aims to better characterize the radio frequency signals in the real world. We collect the dataset at transmitters and receivers to simulate a real-world RFF dataset based on the long-term evolution (LTE). Furthermore, we verify the dataset and confirm its reliability. The dataset and reproducible code of this paper can be downloaded from GitHub link: https://github.com/njuptzsp/XSRPdataset.

Standardized monitoring of BCR::ABL1 mRNA levels is essential for the management of chronic myeloid leukemia (CML) patients. From 2016 to 2021 the European Treatment and Outcome Study for CML (EUTOS) explored the use of secondary, lyophilized cell-based BCR::ABL1 reference panels traceable to the World Health Organization primary reference material to standardize and validate local laboratory tests. Panels were used to assign and validate conversion factors (CFs) to the International Scale and assess the ability of laboratories to assess deep molecular response (DMR). The study also explored aspects of internal quality control. The percentage of EUTOS reference laboratories (n = 50) with CFs validated as optimal or satisfactory increased from 67.5% to 97.6% and 36.4% to 91.7% for ABL1 and GUSB, respectively, during the study period and 98% of laboratories were able to detect MR4.5 in most samples. Laboratories with unvalidated CFs had a higher coefficient of variation for BCR::ABL1IS and some laboratories had a limit of blank greater than zero which could affect the accurate reporting of DMR. Our study indicates that secondary reference panels can be used effectively to obtain and validate CFs in a manner equivalent to sample exchange and can also be used to monitor additional aspects of quality assurance.

this study aims to analyze the impact of data selection to train machine learning models and forecast Bitcoin prices. Specifically, we train elastic net regularization models using two datasets with almost identical total observations. One dataset emphasizes years of observations (depth) over total variables, while the second one emphasizes the number of variables (width) over years of data. Our results suggest that the dataset with more extended historical time series and fewer variables provides a lower forecasting error than the dataset with shorter time series and more variables. Our results may be helpful to practitioners looking to identify data selection strategies to train ML-based forecasting models.

Solid-phase microextraction (SPME)-direct mass spectrometry (MS) has proven to be an efficient tool for the rapid screening and quantitation of target compounds at trace levels. However, it is challenging to perform screening using both positive and negative modes in one analytical run without compromising scanning speed and detection sensitivity. To take advantage of the special geometry of a coated blade spray (CBS) blade, which consists of two flat sides coated with the same SPME coating, we developed a CBS-MS method that enables desorption and ionization to be performed in positive ionization mode on one side of a coated blade and negative ionization mode on the other side of the same blade. By simply flipping the blade 180°, MS analysis in both ionization modes on different sides can be completed in 40 s. Combining this approach with an automated Concept 96-blade-based SPME system allowed analysis for one sample in positive and negative modes to be completed in less than 1 min. The workflow was optimized by using a biocompatible polyacrylonitrile as an undercoating layer and a binder of polyacrylonitrile/hydrophilic-lipophilic balance (HLB) particles, which enabled the rapid analysis of 20 drugs of abuse in saliva samples in both positive and negative modes. The proposed method provided low limits of quantification (between 0.005 and 10 ng/mL), with calibration linear correlation coefficients ⩾ 0.9925, accuracy between 72% and 126%, and relative precision < 15% for three validation points.

COVID-19 is an illness caused by severe acute respiratory syndrome coronavirus 2. Due to its rapid spread, in March 2020 the World Health Organization (WHO) declared pandemic. Since the outbreak of pandemic many governments, scientists, and institutions started to work on new vaccines and finding of new and repurposing drugs. Drug repurposing is an excellent option for discovery of already used drugs, effective against COVID-19, lowering the cost of production, and shortening the period of delivery, especially when preclinical safety studies have already been performed. There are many approved drugs that showed significant results against COVID-19, like ivermectin and hydrochloroquine, including alternative treatment options against COVID-19, utilizing herbal medicine. This article summarized 11 repurposing drugs, their positive and negative health implications, along with traditional herbal alternatives, that harvest strong potential in efficient treatments options against COVID-19, with small or no significant side effects. Out of 11 repurposing drugs, four drugs are in status of emergency approval, most of them being in phase IV clinical trials. The first repurposing drug approved for clinical usage is remdesivir, whereas chloroquine and hydrochloroquine approval for emergency use was revoked by FDA for COVID-19 treatment in June 2020.

The aim of this study is to investigate the environmental risk of long-term metallurgical waste disposal. The investigated site was used for the open storage of lead and zinc waste materials originating from a lead smelter and refinery. Even after remediation was performed, the soil in the close vicinity of the metallurgical waste deposit was heavily loaded with heavy metals and arsenic. The pollutants were bound in various compounds in the form of sulfides, oxides, and chlorides, as well as complex minerals, impacting the pH values of the investigated soil, such that they varied between 2.8 for sample 6 and 7.34 for sample 8. In order to assess the environmental risk, some eight soil samples were analyzed by determining the total metal concentration by acid digestion and chemical fractionation of heavy metals using the BCR sequential extraction method. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine six elements (As, Cd, Cu, Pb, Zn, and Ni). Total concentrations of the elements in the tested soil samples were in the range of 3870.4–52,306.18 mg/kg for As, 2.19–49.84 mg/kg for Cd, 268.03–986.66 mg/kg for Cu, 7.34–114.67 mg/kg for Ni, 1223.13–30,339.74 mg/kg for Pb, and 58.21–8212.99 mg/kg for Zn. The ratio between the mean concentrations of the tested metals was determined in this order: As > Pb > Zn > Cu > Ni > Cd. The BCR results showed that Pb (50.7%), Zn (49.2%), and Cd (34.7%) had the highest concentrations in mobile fractions in the soil compared to the other metals. The contamination factor was very high for Pb (0.09–33.54), As (0.004–195.8), and Zn (0.14–16.06). According to the calculated index of potential environmental risk, it was confirmed that the mobility of Pb and As have a great impact on the environment.

Physics‐informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics‐informed neural network models suffer from several issues and can fail to provide accurate solutions in many scenarios. We discuss a few of these challenges and the techniques, such as the use of Fourier transform, that can be used to resolve these issues. This paper proposes and develops a physics‐informed neural network model that combines the residuals of the strong form and the potential energy, yielding many loss terms contributing to the definition of the loss function to be minimized. Hence, we propose using the coefficient of variation weighting scheme to dynamically and adaptively assign the weight for each loss term in the loss function. The developed PINN model is standalone and meshfree. In other words, it can accurately capture the mechanical response without requiring any labeled data. Although the framework can be used for many solid mechanics problems, we focus on three‐dimensional (3D) hyperelasticity, where we consider two hyperelastic models. Once the model is trained, the response can be obtained almost instantly at any point in the physical domain, given its spatial coordinates. We demonstrate the framework's performance by solving different problems with various boundary conditions.

CLIPPER2 was an 8-year, open-label extension of the phase 3b, 2-year CLIPPER study of the safety and efficacy of etanercept (ETN) in patients (pts) with juvenile idiopathic arthritis (JIA), categorized as extended oligoarticular JIA (eoJIA), enthesitis-related arthritis (ERA), or psoriatic arthritis (PsA).Evaluation of the efficacy of ETN and its effect on health outcomes over 10 years of follow-up were secondary objectives and are reported here.Pts (n=127) with eoJIA (n=60; 2-17 years of age), ERA (n=38; 12-17), or PsA (n=29; 12-17) who received ≥1 ETN dose (0.8 mg/kg once weekly [max, 50 mg]) in CLIPPER were eligible to enter CLIPPER2. The study design has been reported previously.1 Efficacy endpoints included proportions of pts achieving JIA American College of Rheumatology (ACR) 30/50/70/90/100 criteria, Juvenile Arthritis Disease Activity Score (JADAS) inactive disease and clinical remission criteria, and sustained clinical remission (ACR criteria) or JADAS ≤1 for 12 continuous months (mths). Exploratory efficacy endpoints included time to flare following ETN withdrawal (based on ≥30% worsening in ≥3/6 ACR Pedi components, with ≥30% improvement in <2/6 remaining components and ≥2 active joints), and time to re-treatment with ETN.Observed Cases were used (i.e., there was no imputation for missing data) for pts who were in the Active Treatment Period.A total of 109/127 (86%) CLIPPER participants entered CLIPPER2 (n=55 eoJIA, n=31 ERA, n=23 PsA), with 99 (78%) pts continuing in the Active Treatment Period. Overall, 84 (66%) pts completed 120 mths of follow-up; 27 (21%) while actively taking ETN. Thirty (24%) pts entered the Withdrawal Period from the Active Treatment Period (i.e., they discontinued ETN, either by meeting the Wallace definition for clinically inactive disease for at least 6 months on ETN, or by having had a good clinical response and being deemed to benefit from withdrawal in the investigator’s judgment). Of the pts in the Active Treatment Period, over 90% achieved JIA ACR 50 response at all study time points. Sustained JADAS and ACR remission was achieved by 42 (33%) pts and 17 (13%) pts, respectively. The mean improvements from baseline in JADAS disease activity at mth 24 of CLIPPER were largely maintained through CLIPPER2 A total of 109/127 (86%) CLIPPER participants entered CLIPPER2 (n=55 eoJIA, n=31 ERA, n=23 PsA), with 99 (78%) pts continuing in the Active Treatment Period. Overall, 84 (66%) pts completed 120 mths of follow-up; 27 (21%) while actively taking ETN. Thirty (24%) pts entered the Withdrawal Period from the Active Treatment Period (i.e., they discontinued ETN, either by meeting the Wallace definition for clinically inactive disease for at least 6 months on ETN, or by having had a good clinical response and being deemed to benefit from withdrawal in the investigator’s judgment). Of the pts in the Active Treatment Period, over 90% achieved JIA ACR 50 response at all study time points. Sustained JADAS and ACR remission was achieved by 42 (33%) pts and 17 (13%) pts, respectively. The mean improvements from baseline in JADAS disease activity at mth 24 of CLIPPER were largely maintained through CLIPPER2.The low numbers of evaluable pts notwithstanding, efficacy results were consistent with the profile of ETN, and treatment responses were considered clinically meaningful and durable with long-term treatment.[1]Foeldvari I, et al. Arthritis Res Ther 2019;21:125.[2]Trincianti C, et al. Arthritis Rheumatol 2021:73;1966-75.Trial Registration:NCT00962741/NCT01421069Medical writing support was provided by Iain McDonald, PhD, of Engage Scientific Solutions and was funded by Pfizer.Jelena Vojinovic Speakers bureau: Abbvie, Roche, Sandoz, Joke Dehoorne Speakers bureau: Abbvie, Roche, Consultant of: Abbvie, Roche, Violeta Panaviene: None declared, Gordana Susic: None declared, Gerd Horneff Speakers bureau: Chugai, Eli-Lilly, Glaxo Smith and Kline, Janssen, Novartis, Pfizer, Roche and Sobi, Grant/research support from: Novartis, Janssen, Roche, Valda Stanevicha Speakers bureau: Sandoz, Abbvie, Roche, Katarzyna Kobusinska: None declared, Zbigniew Żuber: None declared, Bogna Dobrzyniecka: None declared, Jonathan Akikusa: None declared, Tadej Avcin Speakers bureau: AbbVie, Octapharma and Takeda, Consultant of: AbbVie, Octapharma and Takeda, Alberto Martini Speakers bureau: Aurinia, Bristol Myers and Squibb, Eli-Lilly, EMD Serono, Janssen, Pfizer, Roche, Consultant of: Aurinia, Bristol Myers and Squibb, Eli-Lilly, EMD Serono, Janssen, Pfizer, Roche, Cecilia Borlenghi Shareholder of: Pfizer, Employee of: Pfizer, Edmund Arthur Employee of: Pfizer, Svitlana Y Tatulych Shareholder of: Pfizer, Employee of: Pfizer, Chuanbo Zang Shareholder of: Pfizer, Employee of: Pfizer, Vasileios TSEKOURAS Shareholder of: Pfizer, Employee of: Pfizer, Bonnie Vlahos Shareholder of: Pfizer, Employee of: Pfizer, Nicolino Ruperto Speakers bureau: Ablynx, Amgen, Astrazeneca-Medimmune, Aurinia, Bayer, Bristol Myers and Squibb, Cambridge Healthcare Research (CHR), Celgene, Domain therapeutic, Eli-Lilly, EMD Serono, Glaxo Smith and Kline, Idorsia, Janssen, Novartis, Pfizer, Sobi, UCB., Consultant of: Ablynx, Amgen, Astrazeneca-Medimmune, Aurinia, Bayer, Bristol Myers and Squibb, Cambridge Healthcare Research (CHR), Celgene, Domain therapeutic, Eli-Lilly, EMD Serono, Glaxo Smith and Kline, Idorsia, Janssen, Novartis, Pfizer, Sobi, UCB.

Despite the low rate of neurological deficits following the SARS-COV-2 infection in the pediatric population, children and adolescents who develop multisystem inflammatory syndrome (MIS-C) after being infected with SARS-COV-2 are at a higher risk for neurological abnormalities and brain injury, increasing the risk of adverse cognitive and psychiatric outcome.Given the increased risk of central nervous system impairment we chose to conduct a prospective study looking at the cognitive and psychosocial outcome of patients with MIS-C.Our study included 27 of the 29 patients between 2 to 18 years of age (M = 11.1, SD = 4.4) who were treated for MIS-C from the onset of the SARS-COV-2 pandemic until the beginning of May 2021 at the only tertiary care pediatric immunology center in Slovenia. We assessed these patients 6 months after diagnosis using the age-appropriate Wechsler intelligence scales and a battery of neuropsychological test measuring attention, executive function, memory and fine motor skills. We also asked parents to report on patients’ psychosocial outcome using the Achenbach Child Behavior Checklist.By using Bayesian statistics to take into account parental education and any potential pre-morbid learning difficulties we found no evidence of impairment on measures of intelligence. However, the posterior distribution of scores on neuropsychological measures indicated that a significant proportion of patients scored 1SD bellow expected levels on measures of attention (31%), executive function (28%) and visual memory (35%). Increased symptoms of depression, anxiety and attention difficulties were also reported by parents, although their extent did not rise to a clinically significant level.The findings from our cohort suggest that the cognitive and psychosocial outcome of patients with MIS-C is generally favorable, although up to 35% may experience specific neuropsychological deficits more than 6 months after diagnosis. The most commonly impaired cognitive domains seem to be attention, executive function and visual memory.Funding for this work was provided by the Slovenian Research Agency grant J3-3061 and University Medical Centre grant 20210069. Support was also provided by Dušica Boben and the publisher Center za psihodiagnostična sredstva by providing the local adaptations of psychological assessment tools.David Gosar Speakers bureau: Biogen, Novartis, Mojca Zajc Avramovič: None declared, Nina Emersic: None declared, Mateja Šušterič: None declared, Maja Maša Šömen: None declared, Damjan Osredkar: None declared, Tadej Avcin: None declared

In contrast to adults, children are less likely to develop serious disease upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) but are at increased risk for inflammatory and autoimmune diseases linked to the virus (1). The reported incidence of multisystem inflammatory syndrome in children (MIS-C) varied from 0.2 to 11.4/100,000 persons under 21 years (2,3). It is yet unknown whether MIS-C can recur after SARS-CoV-2 reinfection or COVID-19 vaccination.To estimate the incidence and describe the spectrum of inflammatory and autoimmune diseases linked to SARS-CoV-2 infection and coronavirus (COVID-19) vaccination in pediatric patients from two neighbouring South Central European countries and regions, Slovenia and Friuli Venezia Giulia (FVG), Italy.We performed a multi-centre prospective cohort study of all children and adolescents (under 18 years) newly diagnosed with MIS-C or other inflammatory/autoimmune diseases linked to SARS-CoV-2 infection, who were admitted to the pediatric tertiary care hospitals in Slovenia or FVG, Italy during the period from January 1, 2020, to December 31, 2021. These hospitals serve a combined population of 587,053 children and adolescents. Only patients who had positive anti-SARS-CoV-2 antibodies and/or positive SARS-CoV-2 PCR test within 3 months prior to disease onset were considered for estimating the disease incidence. We obtained the number of patients with serious adverse events (SAE) after COVID-19 vaccination and the number of patients with severe COVID-19 in the same population. This study was conducted as a part of the EU interregional Italy-Slovenia project CATTEDRA (Cross border cooperation for innovative diagnosis of rare diseases in paediatrics).192 children and adolescents were diagnosed with inflammatory and autoimmune diseases linked to SARS-CoV-2 (Figure 1). Median age at diagnosis was 11.9 years (IQR 7.6 -14.7). All included patients were White. Incidence of MIS-C was one in 921 children and adolescents after SARS-CoV-2 infection and one in 5870 of all children and adolescents. Cumulative incidence of MIS-C since the start of the pandemic was 17/100,000 children and adolescents. Until December 31, 2021, 92,139 children and adolescents (15.7 %) received at least one dose of COVID-19 vaccine. Three patients presented with inflammatory/autoimmune disease after COVID-19 vaccination, including 2 patients with MIS-C and one patient with myositis. All 3 had evidence of recent SARS-CoV-2 infection in form of positive anti-N SARS-CoV-2 antibodies. In the same period, 15 children and adolescents were hospitalised with severe COVID-19. Seven patients from our cohort were vaccinated against COVID-19 median 8 months after MIS-C and further 6 patients had a SARS-CoV-2 reinfection 3-14 months after MIS-C. None of them experienced SAE or recurrence of MIS-C.Figure 1.Inflammatory and autoimmune diseases linked to SARS-CoV-2 and severe COVID-19 in pediatric population in Slovenia and FVG, ItalyCOVID-19=coronavirus disease, FVG=Friuli Venezia Giulia region in Italy, MIS-C=multisystem inflammatory syndrome in children, SARS-CoV-2=severe acute respiratory syndrome coronavirus 2MIS-C was the most common manifestation and its incidence in this predominantly white population was higher than previously reported. Based on our limited experience, MIS-C does not seem to recur after SARS-CoV-2 reinfection or COVID-19 vaccination, however long-term data are lacking. Autoimmune diseases were much more common after SARS-CoV-2 infection than after COVID-19 vaccination. Hospitalisations due to MIS-C were seven times as frequent as hospitalisations due to severe COVID-19 in children.[1]Ramaswamy A, et al. Immunity. 2021;54:1083-1095.e7.[2]Belay ED, et al. JAMA Pediatrics. 2021;175:837–45.[3]Lee EH, et al. JAMA Netw Open. 2020;3:e2030280.None declared

CLIPPER2 was an 8-year, open-label extension of the phase 3b, multicenter, 2-year CLIPPER study of the safety and efficacy of etanercept (ETN) in the treatment of patients (pts) with juvenile idiopathic arthritis (JIA) categorized as extended oligoarticular arthritis (eoJIA), enthesitis-related arthritis (ERA), or psoriatic arthritis (PsA).The objective of this analysis was to describe the safety of ETN in this population after 10 years of follow up.Pts (n=127) with eoJIA (2-17 years), ERA, or PsA (each 12-17 years) who received ≥1 ETN dose (0.8 mg/kg once weekly [max, 50 mg]) in CLIPPER were eligible to enter CLIPPER2. The primary outcome measure was the occurrence of malignancy. Long-term safety was assessed as the total incidence of events from CLIPPER baseline (BL) to month (mth) 120, frequency of events per 100 patient-years (EP100PY), and frequency of events in each study year.A total of 109/127 (86%) pts entered CLIPPER2; 99 (78%) continued in the active treatment period. At mth 120, 84 (66%) pts had completed the study; 27 (21%) while actively taking ETN; 7 (6%) had withdrawn from treatment due to low/inactive disease; 5 (4%) had re-started ETN following an earlier withdrawal from treatment; and 45 (35%) had stopped ETN (but remained under observation); 25 (20%) pts permanently discontinued from the CLIPPER2 study. In CLIPPER/CLIPPER2, 1 case of malignancy (Hodgkin’s disease) was reported (1 pt with eoJIA in Year 3). There was 1 case of uveitis (1 pt with eoJIA in Year 8) and 3 of Crohn’s disease (2 pts with ERA, Year 1/Year 6; 1 pt with eoJIA, Year 5). There were 2 cases of opportunistic infections (both herpes zoster), and no deaths. Overall, there were 559 (81.82 EP100PY) treatment-emergent adverse events (TEAEs) excluding infections and injection-site reactions (ISRs). The overall rate of TE serious infections was low (N=14; 2.05 EP100PY) (Table 1), with the most common TE serious infection being gastroenteritis (N=2; 0.29 EP100PY). The most frequently reported TEAEs (N [EP100PY]) were headache (28 [4.10]), arthralgia (24 [3.51]), pyrexia (21 [3.07]), diarrhea (14 [2.05]), and leukopenia (12 [1.76]). Overall, 39 patients reported serious AEs (excluding infections/ISRs). The number and frequency (N [EP100PY]) of TEAEs (excluding infections/ISRs) decreased over the 10-year study period from 193 [173.81] in Year 1 to 9 [27.15] in Year 10. The number and frequency of TE infections and TE serious infections also decreased over the 10-year study period. There was no clear trend of a decrease over time for the incidence of TE serious AEs (Figure 1).Table 1.ETN Safety Summary (from CLIPPER BL to mth 120), N (EP100PY) (FAS)*eoJIA, n=60(EXP=313.667 PY)ERA, n=38(EXP=206.971 PY)PsA, n=29(EXP=162.576 PY)Total, n=12(EXP=683.214 PY)TEAEs†269 (85.76)176 (85.04)114 (70.12)559 (81.82)TE serious AEs†16 (5.10)17 (8.21)7 (4.31)40 (5.85)TE ISRs23 (7.33)29 (14.01)12 (7.38)64 (9.37)TE infections418 (133.26)99 (47.83)155 (95.34)672 (98.36)TE serious infectionsǂ5 (1.59)4 (1.93)5 (3.08)14 (2.05)Opportunistic infections§01 (0.48)1 (0.62)2 (0.29)TEAEs causing withdrawal†7 (2.23)9 (4.35)2 (1.23)18 (2.63)TE infections causing withdrawal2 (0.64)01 (0.62)3 (0.44)*While on active ETN treatment or within 30 days of last dose†Excluding infections/ISRsǂGastroenteritis, 2 (0.29); acute tonsillitis, anal abscess, bronchopneumonia, gastrointestinal infection, helicobacter gastritis, influenza, peritonitis, pharyngitis, pyelocystitis, sepsis, urinary tract infection, viral infection, all 1 (0.15)§Both herpes zosterEXP, exposure to ETN; FAS, full analysis set; n, number of patients; N, number of eventsETN treatment to mth 120 was well tolerated in this patient population and consistent with the known safety profile. Frequency of TEAEs and TE infections decreased over time. Over 10 years, there was 1 reported event of malignancy and the overall rate of TE serious infections was low.[1]NCT00962741/NCT01421069Medical writing support was provided by Iain McDonald, PhD, of Engage Scientific Solutions and was funded by Pfizer.Jelena Vojinovic Speakers bureau: Abbvie, Roche, Sandoz, Joke Dehoorne Speakers bureau: Abbvie, Roche, Consultant of: Abbvie, Roche, Violeta Panaviene: None declared, Gordana Susic: None declared, Gerd Horneff Speakers bureau: Chugai, Eli-Lilly, Glaxo Smith and Kline, Janssen, Novartis, Pfizer, Roche and Sobi, Grant/research support from: Novartis, Janssen, Roche, Valda Stanevicha Speakers bureau: Sandoz, Abbvie, Roche, Katarzyna Kobusinska: None declared, Zbigniew Żuber: None declared, Bogna Dobrzyniecka: None declared, Jonathan Akikusa: None declared, Tadej Avcin Speakers bureau: AbbVie, Octapharma, and Takeda, Consultant of: AbbVie, Alexion, Octapharma, and Takeda, Alberto Martini Speakers bureau: Aurinia, Bristol Myers and Squibb, Eli-Lilly, EMD Serono, Janssen, Pfizer, Roche, Consultant of: Aurinia, Bristol Myers and Squibb, Eli-Lilly, EMD Serono, Janssen, Pfizer, Roche, Cecilia Borlenghi Shareholder of: Pfizer, Employee of: Pfizer, Edmund Arthur Employee of: Pfizer, Svitlana Y Tatulych Shareholder of: Pfizer, Employee of: Pfizer, Chuanbo Zang Shareholder of: Pfizer, Employee of: Pfizer, Bonnie Vlahos Shareholder of: Pfizer, Employee of: Pfizer, Nicolino Ruperto Speakers bureau: Ablynx, Amgen, Astrazeneca-Medimmune, Aurinia, Bayer, Bristol Myers and Squibb, Cambridge Healthcare Research (CHR), Celgene, Domain therapeutic, Eli-Lilly, EMD Serono, Glaxo Smith and Kline, Idorsia, Janssen, Novartis, Pfizer, Sobi, UCB., Consultant of: Ablynx, Amgen, Astrazeneca-Medimmune, Aurinia, Bayer, Bristol Myers and Squibb, Cambridge Healthcare Research (CHR), Celgene, Domain therapeutic, Eli-Lilly, EMD Serono, Glaxo Smith and Kline, Idorsia, Janssen, Novartis, Pfizer, Sobi, UCB.